Browsing by Author "Renda, Andrea"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Publication
    Can the EU Digital Markets Act achieve its goals?
    (IE Center for the Governance of Change, 2022-06-21) Renda, Andrea; https://ror.org/02jjdwm75
    Over the past few years, EU and national regulators accelerated their plans to regulate digital platforms. The EU is not alone in this quest, as many other jurisdictions around the world are considering various options for regulatory intervention in digital markets. Rather than being an ‘outlier’ on this issue, Europe has a chance to set new standards in this domain, which other countries may end up emulating. In this context, the proposed EU Digital Markets Act (DMA) represents a major endeavour to tackle the concentration of economic power in the hands of large-scale Tech companies. One of its most significant features is that it marks a transition from the traditional application of ex post antitrust rules, towards an entirely new ex ante regulatory scrutiny, aimed at systematically preventing possible abuses by those platforms that can be defined as gatekeepers of entire digital ecosystems. This paper explores the pros and cons of this shift, and discusses whether the proposed enforcement of the DMA, concentrated in the hands of the European Commission, is likely to prove sufficiently effective. The author assesses whether the DMA, together with other legislative proposals presented by the European Commission over the past few months (Data Governance Act, Data Act, Digital Services Act, Artificial Intelligence Act, etc.) will pave the way for a more economically and socially sustainable digital ecosystem in the years to come.
  • Thumbnail Image
    Publication
    Data Policy: A Conceptual Framework
    (IE Center for the Governance of Change, 2023-06-01) Renda, Andrea; https://ror.org/02jjdwm75
    The first policy paper offers a conceptual framework to address the elusive economics of data and a decalogue for policymakers to optimize the value of data flows, highlighting the need for tailored approaches based on data's utility function.